A Phylodynamic Artificial Intelligence framework to predict evolution of SARS-CoV-2 variants of concern in Immunocompromised persons with HIV (PhAI-CoV)

用于预测免疫功能低下的 HIV 感染者 (PhAI-CoV) 中关注的 SARS-CoV-2 变异体进化的系统动力学人工智能框架

• 批准号: 10481017
• 负责人: MARIA LUISA ALCAIDE
• 金额: $ 77.67万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-12 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10481017
• 关键词: 2019-nCoV; Acute; Affect; Amino Acids; Artificial Intelligence; Automobile Driving; Biological; COVID-19; COVID-19 pandemic; COVID-19 patient; COVID-19 severity; COVID-19 vaccination; Cessation of life; Cities; Classification; Clinical; Clinical Data; Clinical Management; Country; Data; Databases; Disease; Epidemic; Epidemiologic Monitoring; Event; Evolution; Florida; General Population; Glycoproteins; Goals; HIV; HIV Infections; HIV Seronegativity; Herd Immunity; Hospitalization; Immune Evasion; Immune System Diseases; Immunocompromised Host; Incidence; Individual; Infection; Investigation; Language Development; Lead; Learning; Link; Longitudinal cohort; Measures; Methods; Modeling; Molecular Epidemiology; Mutation; Outcome; Participant; Pathogenesis; Pathogenicity; Patients; Pattern; Persons; Population; Population Dynamics; Probability; Public Health; Receptor Cell; Recording of previous events; Reporting; Research Personnel; SARS-CoV-2 B.1.617.2; SARS-CoV-2 genome; SARS-CoV-2 infection; SARS-CoV-2 variant; Sampling; Site; Surveys; Testing; Training; United States; Vaccinated; Vaccination; Vaccines; Variant; Viral; artificial intelligence algorithm; chronic infection; co-infection; cohort; deep learning; deep learning model; experience; genomic epidemiology; immunosuppressed; infection burden; infection rate; mortality; multidisciplinary; novel; public health relevance; receptor binding; recruit; saliva sample; severe COVID-19; tool; transmission process; unvaccinated; vaccine effectiveness; vaccine hesitancy; variants of concern

Summary The United States (US) is the most affected country worldwide by the ongoing Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic. The availability of effective vaccines had initially slowed down new infections, reducing incidence of severe coronavirus disease 2019 (COVID-19) cases, hospitalization burden, and deaths. Unfortunately, vaccine hesitancy, and the emergence of new, highly transmissible variants of concern (VOCs), such as the Delta variant that has rapidly become the dominant one in the US among both non-vaccinated and vaccine breakthrough cases, have caused a new dramatic epidemic surge in July-August 2021, and are likely to be an ongoing problem hindering epidemic eradication efforts. Although data on increased mortality and worse clinical outcome in people with HIV (PWH) with COVID-19 is somewhat equivocal, recent surveys indicate that PWH has a higher likelihood of severe disease or death than patients without immune dysfunction. Moreover, while most people effectively clear SARS-CoV-2 in 2-4 weeks, several reports of infection in immunosuppressed individuals have shown intra-host emergence of multi-mutational variants, some at sites linked to immune evasion, especially in case of persistent infection. The overarching goal of the proposed project is to investigate SARS-CoV-2 genomes intra-host evolution in the context of HIV infection by developing a phylodynamic and artificial Intelligence framework to assess the emergence and likelihood of SARS-CoV-2 VOC (PhAI-CoV) in immunocompromised PWH. The hypothesis is that SARS-CoV-2 infection in PWH can result in enhanced evolution of viral variants that can efficiently be tracked by phylodynamic analysis and predicted to be VOCs by artificial intelligence algorithms. To test such a hypothesis, we developed three specific aims that will investigate three complementary, albeit independent, issues. We will use a well-characterized cohort of PWH and rigorously collected longitudinal data and samples from patients with SARS-CoV-2 co-infection in Miami, Florida, one of the cities with the highest HIV and SARS-CoV-2 infection burden in the US. In Specific Aim 1 we will recruit and retain n=120 PWH with acute SARS-CoV-2 infection, as well as n=120 matching controls with acute SARS-CoV2 infection but without HIV, and study how COVID-19 disease severity differs by HIV status, depending on SARS-CoV-2 vaccination history and infecting variant. In Specific Aim 2, we will Investigate intra- host SARS-CoV-2 evolution throughout the duration of infection to assess the likelihood of SARS-CoV-2 infection in PWH to result in sustained intra-host evolution leading to the emergence of novel viral variants. In specific Aim 3, we will develop an artificial intelligence algorithm that can predict the likelihood of new variants to be VOCs. Understanding the evolutionary scenarios of SARS-CoV-2 variants emergence within HIV infection and evaluating the probability for increased strain infectivity and/or pathogenicity will provide a crucial tool for planning and implementing public health measures before transmission occurs in the general population.

概括 美国是全球受严重急性呼吸系统疾病影响最严重的国家 综合症冠状病毒 2 (SARS-CoV-2) 大流行。有效疫苗的供应最初已经放缓 减少新增感染，减少 2019 年冠状病毒病 (COVID-19) 重症病例和住院率的发生率 负担和死亡。不幸的是，疫苗犹豫不决以及新的高传染性变种的出现 值得关注的挥发性有机化合物（VOC），例如 Delta 变种，它已迅速成为美国在这两种物质中占主导地位的变种 未接种病例和疫苗突破病例，造成7-8月新一轮疫情激增 2021 年，这可能是阻碍消除流行病努力的一个持续问题。尽管数据有所增加 近期，HIV 感染者 (PWH) 感染 COVID-19 的死亡率和临床结果较差的情况有些模棱两可 调查表明，与没有免疫力的患者相比，感染者患有严重疾病或死亡的可能性更高 功能障碍。此外，虽然大多数人在 2-4 周内有效清除 SARS-CoV-2，但一些感染报告 在免疫抑制个体中，已显示出宿主内出现多突变变异，其中一些发生在位点 与免疫逃避有关，尤其是在持续感染的情况下。拟议项目的总体目标 的目的是通过开发一种 用于评估 SARS-CoV-2 VOC 出现和可能性的系统动力学和人工智能框架 (PhAI-CoV) 存在于免疫功能低下的感染者中。假设感染者感染 SARS-CoV-2 可导致 增强病毒变体的进化，可以通过系统动力学分析有效追踪并预测 通过人工智能算法检测 VOC。为了检验这样的假设，我们制定了三个具体目标 研究三个互补但独立的问题。我们将使用一组特征良好的感染者卫生保健人员 并严格收集了迈阿密 SARS-CoV-2 合并感染患者的纵向数据和样本， 佛罗里达州是美国 HIV 和 SARS-CoV-2 感染负担最高的城市之一。在具体目标 1 中，我们 将招募并保留 n=120 名患有急性 SARS-CoV-2 感染的 PWH，以及 n=120 名患有急性 SARS-CoV-2 感染的对照对照 急性 SARS-CoV2 感染但没有 HIV，并研究 COVID-19 疾病严重程度如何因 HIV 状态而异， 取决于 SARS-CoV-2 疫苗接种史和感染变体。在具体目标 2 中，我们将调查内部 宿主 SARS-CoV-2 在整个感染过程中的进化，以评估 SARS-CoV-2 感染的可能性 在 PWH 中导致持续的宿主内进化，从而导致新病毒变体的出现。具体来说 目标3，我们将开发一种人工智能算法，可以预测新变体出现的可能性 挥发性有机化合物。了解 HIV 感染中 SARS-CoV-2 变体出现的进化情景 评估菌株感染性和/或致病性增加的可能性将为 在普通人群中发生传播之前规划和实施公共卫生措施。